AUTHOR=Zheng Wenxuan , Li Ruiding , Zhou Yang , Shi Fengcui , Song Yao , Liao Yanting , Zhou Fan , Zheng Xiaohua , Lv Jingwen , Li Quanyang 

TITLE=Effect of dietary protein content shift on aging in elderly rats by comprehensive quantitative score and metabolomics analysis

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2022.1051964

DOI=10.3389/fnut.2022.1051964

ISSN=2296-861X

ABSTRACT=In the protein nutrition strategy of middle-aged and elderly people, some believe that low protein is good for health, while others believe high protein is good for health. Facing the contradictory situation, the following hypothesis is proposed. There is a process of change from lower to higher ratio of protein nutritional requirements that are good for health in the human body after about 50 years of age, and the age at which the switch occurs is around 65 years of age. Hence, in this study, 50, 25-month-old male rats were treated with different protein nutritional modalities while maintaining energy constant. The samples were examined for aging-related indicators, and a newly comprehensive quantitative score was generated using principal component analysis. The results showed that Model1 (switching from a low-protein diet to a high-protein diet at week 4) group had a higher score than the low-protein diet, high-protein diet, and control groups, while Model2 group (switching from a low-protein diet to a high-protein diet at week 7) had the lowest score. This indicates that a sudden increase in protein intake can have significant negative effects in the short term. The low-to high-protein dietary pattern shift improved overall health only after a certain adaptation period. Furthermore, 1H NMR untargeted serum and fecal metabolomics were used to explore the effects of different proteins on health. The differential metabolites in serum and feces were determined by orthogonal partial least squares discriminant analysis, and 15 differential metabolites, significantly associated with protein intake, were identified by Spearman's correlation analysis. Among the fecal metabolites, 10 were positively correlated and 3 were negatively correlated. In the serum, tyrosine and lactate levels were positively correlated, and acetate levels were negatively correlated. MetaboAnalyst analysis identified that the metabolic pathways influenced by protein intake were mainly related to amino acid and carbohydrate metabolism. The results of metabolomic analysis elucidate the mechanisms underlying the preceding effects to some degree. These efforts not only contribute to a unified protein nutrition strategy but also positively impact the building of a wiser approach to protein nutrition, thereby helping middle-aged and older populations achieve healthy aging.